Triple

T2294721
Position Surface form Disambiguated ID Type / Status
Subject David O. Selznick E51584 entity
Predicate notableWork P4 FINISHED
Object Gone with the Wind E8652 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Gone with the Wind | Statement: [David O. Selznick, notableWork, Gone with the Wind]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gone with the Wind
Context triple: [David O. Selznick, notableWork, Gone with the Wind]
  • A. Gone with the Wind chosen
    Gone with the Wind is a landmark 1939 American epic historical romance film set during the American Civil War and Reconstruction, renowned for its grand scale, cultural impact, and enduring popularity.
  • B. Light of the South
    Light of the South is the English rendering of the Japanese name given to Singapore during its World War II occupation.
  • C. National Velvet
    National Velvet is a classic 1944 family sports drama film about a young girl and her horse competing in the Grand National steeplechase.
  • D. Red Dust
    Red Dust is a 1932 American pre-Code romantic drama film starring Clark Gable and Jean Harlow, set on a rubber plantation in French Indochina.
  • E. Red Dust
    Red Dust is a film associated with director Tom Hooper, known as one of his earlier dramatic works before his rise to international prominence.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a88b09c644819090b503456d96bf70 completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc5da667881909186adf23a2bd45b completed March 7, 2026, 6:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea870018481908780ba79a0ddd5c7 completed March 9, 2026, 11:01 a.m.
Created at: March 4, 2026, 7:49 p.m.